FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics

ArXiv ID: 2411.12748 “View on arXiv”

Authors: Unknown

Abstract

Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.

Keywords: Time Series Forecasting, Bi-LSTM, FinBERT, Sentiment Analysis, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (Bi-LSTM, FinBERT transformers) requiring knowledge of neural networks and sequence modeling. It demonstrates strong empirical rigor by using specific datasets (Bitcoin/Ethereum news and price data), comparing against multiple benchmarks, and mentioning intra-day performance analysis, suggesting thorough backtesting readiness.

  flowchart TD
    A["Research Goal: Predict Crypto Volatility"] --> B["Data Collection & Preprocessing"]
    B --> C["Market Data & News Sentiment"]
    C --> D["Computational Process: Hybrid Model"]
    subgraph D ["FinBERT-BiLSTM Architecture"]
        D1["FinBERT: Extracts Sentiment Features"]
        D2["Bi-LSTM: Captures Temporal Dependencies"]
    end
    D1 --> D2
    D2 --> E["Forecast Price & Volatility"]
    E --> F["Key Findings & Outcomes"]
    F --> G["Improved Accuracy vs. Traditional Models"]
    F --> H["Enhanced Risk Management Strategies"]